Automatic Speech Recognition (ASR) systems typically output unsegmented transcripts without punctuation.
Restoring punctuations is an important step in processing transcribed speech.
[1]} showed that the absence of punctuations in transcripts affect readability as much as a
significant word error rate. Downstream tasks such as neural machine translation [2]}, sentiment analysis [3]} and information extraction [4]} also benefit from having clausal boundaries.
In this paper we present models for automatic punctuation restoration for English and Hungarian.
Our work is based on a state-of-the-art model proposed by [5]}, which uses pretrained contextualized language models [6]}.
